A shuttle system has been developed to genetically encode unnatural amino acids in mammalian cells using aminoacyl-tRNA synthetases (aaRSs) evolved in E. coli. A pyrrolysyl-tRNA synthetase (PylRS) mutant was evolved in E. coli that selectively aminoacylates a cognate nonsense suppressor tRNA with a photocaged lysine derivative. Transfer of this orthogonal tRNA-aaRS pair into mammalian cells made possible the selective incorporation of this unnatural amino acid into proteins.

In vivo incorporation of isotopically labeled unnatural amino acids into large proteins drastically reduces the complexity of nuclear magnetic resonance (NMR) spectra. Incorporation is accomplished by co-expressing an orthogonal tRNA/aminoacyl-tRNA synthetase pair specific for the unnatural amino acid added to the media and the protein of interest with a TAG amber codon at the desired incorporation site. To demonstrate the utility of this approach for NMR studies, 2-amino-3-(4-(trifluoromethoxy) phenyl) propanoic acid (OCF 3 Phe), 13 C/ 15 N-labeled p-methoxyphenylalanine (OMePhe), and 15 N-labeled o-nitrobenzyl-tyrosine (oNBTyr) were incorporated individually into 11 positions around the active site of the 33 kDa thioesterase domain of human fatty acid synthase (FAS-TE). In the process, a novel tRNA synthetase was evolved for OCF 3 Phe. Incorporation efficiencies and FAS-TE yields were improved by including an inducible copy of the respective aminoacyl-tRNA synthetase gene on each incorporation plasmid. Using only between 8 and 25 mg of unnatural amino acid, typically 2 mg of FAS-TE, sufficient for one 0.1 mM NMR sample, were produced from 50 mL of E. coli culture grown in rich media. Singly labeled protein samples were then used to study the binding of a tool compound. Chemical shift changes in 1 H-15 N, 1 H-13 C HSQC and 19 F NMR spectra of the different single site mutants consistently identified the binding site and the effect of ligand binding on conformational exchange of some of the residues. OMePhe or OCF 3 Phe mutants of an active site tyrosine inhibited binding; incorporating 15 N-Tyr at this site through UV-cleavage of the nitrobenzyl-photocage from oNBTyr re-established binding. These data suggest not only robust methods for using unnatural amino acids to study large proteins by NMR but also establish a new avenue for the site-specific labeling of proteins at individual residues without altering the protein sequence, a feat that can currently not be accomplished with any other method.

SummaryTuberculosis continues to be a global health threat, making bicyclic nitroimidazoles an important new class of therapeutics. A deazaflavin-dependent nitroreductase (Ddn) from Mycobacterium tuberculosis catalyzes the reduction of nitroimidazoles such as PA-824, resulting in intracellular release of lethal reactive nitrogen species. The N-terminal 30 residues of Ddn are functionally important but are flexible or access multiple conformations, preventing structural characterization of the full-length, enzymatically active enzyme. Several structures were determined of a truncated, inactive Ddn protein core with and without bound F420 deazaflavin coenzyme as well as of a catalytically competent homolog from Nocardia farcinica. Mutagenesis studies based on these structures identified residues important for binding of F420 and PA-824. The proposed orientation of the tail of PA-824 toward the N terminus of Ddn is consistent with current structure-activity relationship data.

A large number of amino acids other than the canonical amino acids can now be easily incorporated in vivo into proteins at genetically encoded positions. The technology requires an orthogonal tRNA/aminoacyl-tRNA synthetase pair specific for the unnatural amino acid that is added to the media while a TAG amber or frame shift codon specifies the incorporation site in the protein to be studied. These unnatural amino acids can be isotopically labeled and provide unique opportunities for site-specific labeling of proteins for NMR studies. In this perspective, we discuss these opportunities including new photocaged unnatural amino acids, outline usage of metal chelating and spin-labeled unnatural amino acids and expand the approach to in-cell NMR experiments.

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure and dynamics. More recently, Hydrogen Exchange Mass Spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from Molecular Dynamics (MD) simulation snapshots is used to determine partitioning over bonded and non-bonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for Fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.